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An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis
The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011142/ https://www.ncbi.nlm.nih.gov/pubmed/31883309 http://dx.doi.org/10.1111/jcmm.14918 |
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author | Xiong, Yaoyi Yuan, Lushun Xiong, Jing Xu, Huimin Luo, Yongwen Wang, Gang Ju, Lingao Xiao, Yu Wang, Xinghuan |
author_facet | Xiong, Yaoyi Yuan, Lushun Xiong, Jing Xu, Huimin Luo, Yongwen Wang, Gang Ju, Lingao Xiao, Yu Wang, Xinghuan |
author_sort | Xiong, Yaoyi |
collection | PubMed |
description | The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making. |
format | Online Article Text |
id | pubmed-7011142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70111422020-02-18 An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis Xiong, Yaoyi Yuan, Lushun Xiong, Jing Xu, Huimin Luo, Yongwen Wang, Gang Ju, Lingao Xiao, Yu Wang, Xinghuan J Cell Mol Med Original Articles The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making. John Wiley and Sons Inc. 2019-12-28 2020-02 /pmc/articles/PMC7011142/ /pubmed/31883309 http://dx.doi.org/10.1111/jcmm.14918 Text en © 2019 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Xiong, Yaoyi Yuan, Lushun Xiong, Jing Xu, Huimin Luo, Yongwen Wang, Gang Ju, Lingao Xiao, Yu Wang, Xinghuan An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis |
title | An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis |
title_full | An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis |
title_fullStr | An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis |
title_full_unstemmed | An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis |
title_short | An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis |
title_sort | outcome model for human bladder cancer: a comprehensive study based on weighted gene co‐expression network analysis |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011142/ https://www.ncbi.nlm.nih.gov/pubmed/31883309 http://dx.doi.org/10.1111/jcmm.14918 |
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